Learning from noisy labels with deep neural networks: A survey
Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …
amounts of big data. However, the quality of data labels is a concern because of the lack of …
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …
growing interest in obtaining such datasets for medical image analysis applications …
Selective-supervised contrastive learning with noisy labels
S Li, X ** for learning with noisy labels
The memorization effect of deep neural network (DNN) plays a pivotal role in many state-of-
the-art label-noise learning methods. To exploit this property, the early stop** trick, which …
the-art label-noise learning methods. To exploit this property, the early stop** trick, which …
Dividemix: Learning with noisy labels as semi-supervised learning
Deep neural networks are known to be annotation-hungry. Numerous efforts have been
devoted to reducing the annotation cost when learning with deep networks. Two prominent …
devoted to reducing the annotation cost when learning with deep networks. Two prominent …
Symmetric cross entropy for robust learning with noisy labels
Training accurate deep neural networks (DNNs) in the presence of noisy labels is an
important and challenging task. Though a number of approaches have been proposed for …
important and challenging task. Though a number of approaches have been proposed for …
Normalized loss functions for deep learning with noisy labels
Robust loss functions are essential for training accurate deep neural networks (DNNs) in the
presence of noisy (incorrect) labels. It has been shown that the commonly used Cross …
presence of noisy (incorrect) labels. It has been shown that the commonly used Cross …